A methodology to improve simulation of multibody systems using estimation techniques

This paper presents a methodology for state estimation and accuracy improvement of computer simulations of computer aided engineering (CAE) models based on prediction and correction state estimation techniques and sensing. The aim is to simulate the dynamic behaviour of a real system, which can be sensed, and obtain values of states that are not measurable due to economic or technical limitations. This methodology can be applied to both optimization of design processes and on-line control of complex systems. State estimation techniques are currently used only on mathematical models, where the relationships among system variables are expressed by means of mathematical language, making state observer implementation possible but leading to limitations in system modelling and knowledge. Favoured over mathematical models, multibody CAE models (created by means of computer-aided engineering software) have become the essential tool for complex system development, simulation, analysis, optimization and control, such as multibody systems; one of their main advantages is the ease and flexibility in creating and modifying them, allowing the faithful modelling of complex systems

The use of systems engineering principles for the integration of existing models and simulations

With the rise in computational power, the prospect of simulating a complex engineering system with a high degree of accuracy and in a meaningful way is becoming a real possibility. Modelling and simulation have become ubiquitous throughout the engineering life cycle, as a consequence there are many thousands of existing models and simulations that are potential candidates for integration. This work is concerned with ascertaining if systems engineering principles are of use in the support of virtual testing, from desire to test, designing experiments, specifying simulations, selecting models and simulations, integrating component parts, verifying that the work is as specified, and validating that any outcomes are meaningful. A novel representation of systems engineering framework is proposed and forms the bases for the methods that were developed. It takes the core systems engineering principles and expresses them in a way that can be implemented in a variety of ways. An end to end process for virtual testing with the potential to use existing models and simulations is proposed, it provides structure and order to the testing task. A key part of the proposed process is the recognition that models and simulations requirements are different from those of the system being designed, and hence a modelling and simulation specific writing guide is produced. The automation of any engineering task has the potential to reduce the time to market of the final product, for this reason the potential of natural language processing technology to hasten the proposed processes was investigated. Two case studies were selected to test and demonstrate the potential of the novel approach, the first being an investigation into material selection for a squash ball, and the second being automotive in nature concerned with combining steering and braking systems. The processes and methods indicated their potential value, especially in the automotive case study where inconsistences were identified that could have otherwise affected the successful integration. This capability, combined with the verification stages, improves the confidence of any model and simulation integration. The NLP proof of concept software also demonstrated that such technology has value in the automation of integration. With further testing and development there is the possibility to create a software package to guide engineers through the difficult task of virtual testing. Such a tool would have the potential to drastically reduce the time to market of complex products

Parallel surrogate detection in large-scale simulations

Simulation has become a useful approach in scientific computing and engineering for its ability to model real natural or human systems. In particular, for complex systems such as hurricanes, wildfire disasters, and real-time road traffic, simulation methods are able to provide researchers, engineers and decision makers predicted values in order to help them to take appropriate actions. For large-scale problems, the simulations usually take a lot of time on supercomputers, thus making real-time predictions more difficult. Approximation models that mimic the behavior of simulation models but are computationally cheaper, namely surrogate models , are desired in such scenarios. In the thesis, a framework for scalable surrogate detection in large-scale simulations is presented with the basic idea of using functions to represent functions . The following issues are discussed in the thesis: i) the data mining approaches to detecting and optimizing the surrogate models; ii) the scalable and automated workflow of constructing surrogate models from large-scale simulations; and iii) the system design and implementation with the application of storm surge simulations in the occurrence of hurricanes

Model Driven Combat Effectiveness Simulation Systems Engineering

Model-driven engineering has become popular in the combat effectiveness simulation systems engineering during these last years. It allows to systematically develop a simulation model in a composable way. However, implementing a conceptual model is really a complex and costly job if this is not guided under a well-established framework. Hence this study attempts to explore methodologies for engineering the development of simulation models. For this purpose, we define an ontological metamodelling framework. This framework starts with ontology-aware system conceptual descriptions, and then refines and transforms them toward system models until they reach final executable implementations. As a proof of concept, we identify a set of ontology-aware modelling frameworks in combat systems specification, then an underwater targets search scenario is presented as a motivating example for running simulations and results can be used as a reference for decision-making behaviors

Modelling and simulating in systems biology: an approach based on multi-agent systems

Systems Biology is an innovative way of doing biology recently raised in bio-informatics contexts, characterised by the study of biological systems as complex systems with a strong focus on the system level and on the interaction dimension. In other words, the objective is to understand biological systems as a whole, putting on the foreground not only the study of the individual parts as standalone parts, but also of their interaction and of the global properties that emerge at the system level by means of the interaction among the parts. This thesis focuses on the adoption of multi-agent systems (MAS) as a suitable paradigm for Systems Biology, for developing models and simulation of complex biological systems. Multi-agent system have been recently introduced in informatics context as a suitabe paradigm for modelling and engineering complex systems. Roughly speaking, a MAS can be conceived as a set of autonomous and interacting entities, called agents, situated in some kind of nvironment, where they fruitfully interact and coordinate so as to obtain a coherent global system behaviour. The claim of this work is that the general properties of MAS make them an effective approach for modelling and building simulations of complex biological systems, following the methodological principles identified by Systems Biology. In particular, the thesis focuses on cell populations as biological systems. In order to support the claim, the thesis introduces and describes (i) a MAS-based model conceived for modelling the dynamics of systems of cells interacting inside cell environment called niches. (ii) a computational tool, developed for implementing the models and executing the simulations. The tool is meant to work as a kind of virtual laboratory, on top of which kinds of virtual experiments can be performed, characterised by the definition and execution of specific models implemented as MASs, so as to support the validation, falsification and improvement of the models through the observation and analysis of the simulations. A hematopoietic stem cell system is taken as reference case study for formulating a specific model and executing virtual experiments

Regenerative life support system research and concepts

Life support systems that involve recycling of atmospheres, water, food and waste are so complex that models incorporating all the interactions and relationships are vital to design, development, simulations, and ultimately to control of space qualified systems. During early modeling studies, FORTRAN and BASIC programs were used to obtain numerical comparisons of the performance of different regenerative concepts. Recently, models were made by combining existing capabilities with expert systems to establish an Intelligent Design Support Environment for simpliflying user interfaces and to address the need for the engineering aspects. Progress was also made toward modeling and evaluating the operational aspects of closed loop life support systems using Time-step and Dynamic simulations over a period of time. Example models are presented which show the status and potential of developed modeling techniques. For instance, closed loop systems involving algae systeMs for atmospheric purification and food supply augmentation, plus models employing high plants and solid waste electrolysis are described and results of initial evaluations are presented

An evolutionary complex systems decision-support tool for the management of operations

Purpose - The purpose of this is to add both to the development of complex systems thinking in the subject area of operations and production management and to the limited number of applications of computational models and simulations from the science of complex systems. The latter potentially offer helpful decision-support tools for operations and production managers. Design/methodology/approach - A mechanical engineering firm was used as a case study where a combined qualitative and quantitative methodological approach was employed to extract the required data from four senior managers. Company performance measures as well as firm technologies, practices and policies, and their relation and interaction with one another, were elicited. The data were subjected to an evolutionary complex systems (ECS) model resulting in a series of simulations. Findings - The findings highlighted the effects of the diversity in management decision making on the firm's evolutionary trajectory. The CEO appeared to have the most balanced view of the firm, closely followed by the marketing and research and development managers. The manufacturing manager's responses led to the most extreme evolutionary trajectory where the integrity of the entire firm came into question particularly when considering how employees were utilised. Research limitations/implications - By drawing directly from the opinions and views of managers, rather than from logical "if-then" rules and averaged mathematical representations of agents that characterise agent-based and other self-organisational models, this work builds on previous applications by capturing a micro-level description of diversity that has been problematical both in theory and application. Practical implications - This approach can be used as a decision-support tool for operations and other managers providing a forum with which to explore: the strengths, weaknesses and consequences of different decision-making capacities within the firm; the introduction of new manufacturing technologies, practices and policies; and the different evolutionary trajectories that a firm can take. Originality/value - With the inclusion of "micro-diversity", ECS modelling moves beyond the self-organisational models that populate the literature but has not as yet produced a great many practical simulation results. This work is a step in that direction

Simulating control of the ankle joint

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 33).Computing environments such as Matlab that are conventionally used to simulate dynamics of rigid body systems can be used to model interactions between the system and its environment. However, creating these simulations using Matlab or an equivalent is difficult and there is a need for a more convenient simulation environment for such problems. Two alternative programs, PyODE and OpenSim, were explored to evaluate their ability to fill this need. Models and simulations of the human ankle were created in PyODE. This program is useful for creating simple models where the programmer desires a high level of control over model parameters. Simulations of the ankle kicking a ball and taking a step were created to examine the effect of joint stiffness on these motions and help determine the usefulness of ODE as a simulation tool. Pre-existing models were analyzed in OpenSim. OpenSim is specifically designed for analyzing biomechanical systems. It allows for more complex models to be created but the user has more limited control over the model parameters.by Rebecca Vasquez.S.B

PREDICTIVE MATURITY OF INEXACT AND UNCERTAIN STRONGLY COUPLED NUMERICAL MODELS

The Computer simulations are commonly used to predict the response of complex systems in many branches of engineering and science. These computer simulations involve the theoretical foundation, numerical modeling and supporting experimental data, all of which contain their associated errors. Furthermore, real-world problems are generally complex in nature, in which each phenomenon is described by the respective constituent models representing different physics and/or scales. The interactions between such constituents are typically complex in nature, such that the outputs of a particular constituent may be the inputs for one or more constituents. Thus, the natural question then arises concerning the validity of these complex computer model predictions, especially in cases where these models are executed in support of high-consequence decision making. The overall accuracy and precision of the coupled system is then determined by the accuracy and precision of both the constituents and the coupling interface. Each constituent model has its own uncertainty and bias error. Furthermore, the coupling interface also brings in a similar spectrum of uncertainties and bias errors due to unavoidably inexact and incomplete data transfer between the constituents. This dissertation contributes to the established knowledge of partitioned analysis by investigating the numerical uncertainties, validation and uncertainty quantification of strongly coupled inexact and uncertain models. The importance of this study lies in the urgent need for gaining a better understanding of the simulations of coupled systems, such as those in multi-scale and multi-physics applications, and to identify the limitations due to uncertainty and bias errors in these models